When viewed from the outside, a human brain appears as a volume
with a highly wrinkled surface having numerous long crevices.
Sulcal fundi are 3D curves that lie in the depths of the
cerebral cortex;
informally, the fundus of a sulcus is the curve of maximal
average depth that spans the length of the sulcus.
The sulcal fundi serve as anatomical landmarks, `segmenting'
the cortex into functionally distinct regions. They
are often used as landmarks for downstream computations in
brain imaging
and can be used in creating deformation fields for warping the
cortical
surfaces of different brains onto one another.

Cortical sulci and sulcal fundi have traditionally been
manually identified by
labeling voxels in an MRI brain volume using a GUI that
displays only three
orthogonal 2D brain slices. This process is extremely tedious
and time
consuming and, not surprisingly, prone to human error.
Given the large number of high resolution MRI datasets
currently
available for analysis, automatic and objective extraction and
labeling of
cortical sulci has become a necessity. IMA industrial
postdoc Chiu Yen Kao and collaborators Michael Hofer (TU
Vienna),
Guillermo Sapiro (Minnesota), Josh Stern (Minnesota), and
Daniel Rottenberg
(University of Minnesota and VA Medical Center),
have developed an automatic sulcal extraction method that
promises to
improve the quality and
reproducibility of the process as well as yielding considerable
time savings.

An outer hull surface is computed from a mesh representation
of the graymatter (GM) surface by applying a
morphological closing operation to the level set function.
After the outer hull surface is obtained, the
the geodesic depth (distance) for any given point on the pial
surface
to the outer hull surface is calculated. This results in
the association of a sulcal depth estimate with each mesh
triangle;
the sulcal regions are determined using a depth threshold of
2.5mm.
The connected components are identified using a labeling
algorithm
and the results are run through a thinning algorithm, yielding
a skeleton of
each connected component. The extracted sulcal fundi are
represented as
polylines that are further smoothed by an algorithm that
minimizes a
counterpart to the cubic spline energy for curves on surfaces.